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dc.contributor.authorSharma, Alok
dc.contributor.authorPaliwal, Kuldip K
dc.date.accessioned2017-12-07T00:02:57Z
dc.date.available2017-12-07T00:02:57Z
dc.date.issued2015
dc.identifier.issn0925-2312
dc.identifier.doi10.1016/j.neucom.2014.09.051
dc.identifier.urihttp://hdl.handle.net/10072/125050
dc.description.abstractThe regularized linear discriminant analysis (RLDA) technique is one of the popular methods for dimensionality reduction used for small sample size problems. In this technique, regularization parameter is conventionally computed using a cross-validation procedure. In this paper, we propose a deterministic way of computing the regularization parameter in RLDA for small sample size problem. The computational cost of the proposed deterministic RLDA is significantly less than the cross-validation based RLDA technique. The deterministic RLDA technique is also compared with other popular techniques on a number of datasets and favorable results are obtained.
dc.description.peerreviewedYes
dc.languageEnglish
dc.language.isoeng
dc.publisherElsevier
dc.relation.ispartofpagefrom207
dc.relation.ispartofpageto214
dc.relation.ispartofissuePart 1
dc.relation.ispartofjournalNeurocomputing
dc.relation.ispartofvolume151
dc.subject.fieldofresearchInformation and Computing Sciences not elsewhere classified
dc.subject.fieldofresearchInformation and Computing Sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchPsychology and Cognitive Sciences
dc.subject.fieldofresearchcode089999
dc.subject.fieldofresearchcode08
dc.subject.fieldofresearchcode09
dc.subject.fieldofresearchcode17
dc.titleA deterministic approach to regularized linear discriminant analysis
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.description.versionAccepted Manuscript (AM)
gro.facultyGriffith Sciences, Griffith School of Engineering
gro.rights.copyright© 2015 Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence (http://creativecommons.org/licenses/by-nc-nd/4.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
gro.hasfulltextFull Text
gro.griffith.authorPaliwal, Kuldip K.
gro.griffith.authorSharma, Alok


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